Custom Tables: Options Tab

This feature requires the Custom Tables option.

To access the Options tab of the Custom Tables dialog box:

  1. From the menus, choose:

    Analyze > Tables > Custom Tables...

  2. Click the Options tab.
Data Cell Appearance
Controls what is displayed in empty cells and cells for which statistics cannot be computed.
Empty cells
For table cells that contain no cases (cell count of 0), you can select one of three display options: zero, blank, or a text value that you specify. The text value can be up to 255 characters long.
Statistics that cannot be computed
Text that is displayed if a statistic cannot be computed (for example, the mean for a category with no cases). The text value can be up to 255 characters long. The default value is a period (.).
Width for Data Columns
Controls minimum and maximum column width for data columns. This setting does not affect columns widths for row labels.
TableLook settings
Uses the data column width specification from the current default TableLook. You can create your own custom default TableLook to use when new tables are created, and you can control both row label column and data column widths with a TableLook.
Overrides the default TableLook settings for data column width. Specify the minimum and maximum data column widths for the table and the measurement unit: points, inches, or centimeters.
Missing Values for Scale Variables
For tables with two or more scale variables, controls the handling of missing data for scale variable statistics.
Maximize use of available data (variable-by-variable deletion)
All cases with valid values for each scale variable are included in summary statistics for that scale variable.
Use consistent case base across scale variables (listwise deletion)
Cases with missing values for any scale variables in the table are excluded from the summary statistics for all scale variables in the table.
Effective Base
If you have a variable that represents adjustment weights rather than frequency weights, you can use that variable as an effective base weight variable. The concept of effective base or effective sample size weighting is based in methods for the analysis of data from complex samples. An effective base weight allows for approximate handling of statistical inference in analysis that involves ad hoc adjustments to data from simple random sampling designs by using adjustment weights.
  • The effective base weight affects weighted summary statistics values and column means and column proportions significance tests.
  • If weighting is turned on for the dataset, the dataset weight variable is ignored and results are weighted by the effective base weight variable.
  • The effective base weight variable must be numeric.
  • Cases with negative weight values, a weight value of 0, or missing weight values are excluded from all results.
Count duplicate responses for multiple category sets
A duplicate response is the same response for two or more variables in the multiple category set. By default, duplicate responses are not counted.
Hide small counts
You can choose to hide counts that are less than a specified integer.
  • Hidden values are displayed as <N, where N is the specified integer.
  • The specified integer must be greater than or equal to 2.
  • If weighting is turned on for the dataset or an effective base weight variable is specified, the weighted value is used.

Weights and Rounding

  • If you use Data > Weight Cases to weight cases, non-integer weights are rounded at the cell or category level for significance tests, confidence intervals, and standard errors.
  • If you select Use effective base weight variable, non-integer weight values in the selected weight variable are not rounded.
  • If both are specified, the effective base weight variable is used, and non-integer weights are not rounded.